Ken Ohara

Osaka Prefecture University, Sakai, Ōsaka, Japan

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Publications (8)0 Total impact

  • K. Ohara, Y. Nojima, H. Ishibuchi
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    ABSTRACT: We propose a route guidance method for guiding automobiles in road traffic systems, which is based on traffic information sharing through inter-vehicle communication (IVC). The proposed method does not require a centralized traffic information system. That is, a huge infrastructure is not required in the proposed method. Each driver, however, can utilize the latest traffic information using IVC. Through computational experiments, we show that good results are obtained from traffic information sharing when the percentage of drivers with the proposed method is low. When the percentage of such drivers is high, good results are not obtained because the same traffic information is shared by many drivers. In this case, many drivers tend to choose the same route, which degrades the overall traffic flow.
    Intelligent Control, 2007. ISIC 2007. IEEE 22nd International Symposium on; 11/2007
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    ABSTRACT: We have examined the effect of spatial structures on the evolution of iterated prisoner's dilemma (IPD) game strategies. In our former study, we used two neighborhood structures, which follow the concept of structured demes. One is for the interaction among players through the IPD game. A player in each cell in a grid-world plays against its neighbors defined by this neighborhood structure. The other is for the mating of strategies by genetic operations. A new strategy for a player is generated by genetic operations from a pair of parent strings, which are selected from its neighbors defined by the second neighborhood structure. In this paper, we extend our IPD game simulation to a more realistic problem while keeping the simplicity of the original IPD game. We employ a stochastic strategy represented by a string of real numbers between 0 and 1. Each real number in the string denotes the probability of cooperation. We examine the effects of spatial structures on the evolution of IPD game strategies with probabilistic decision making in various payoff matrices. From simulation results, it is shown that cooperative behavior is evolved only when the interaction neighborhood is small and the mating neighborhood is also small for some payoff matrices.
    Evolutionary Computation, 2007. CEC 2007. IEEE Congress on; 10/2007
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    ABSTRACT: In the design of evolutionary multiobjective optimization (EMO) algorithms, it is important to strike a balance between diversity and convergence. Traditional mask-based crossover operators for binary strings (e.g., one-point and uniform) tend to decrease the diversity of solutions in EMO algorithms while they improve the convergence to the Pareto front. This is because such a crossover operator, which is called geometric crossover, always generates an offspring in the segment between its two parents under the Hamming distance in the genotype space. That is, the sum of the distances from the generated offspring to its two parents is always equal to the distance between the parents. In this paper, first we propose a non-geometric binary crossover operator to generate an offspring outside the segment between its parents. Next we examine the effect of the use of non-geometric binary crossover on single-objective genetic algorithms. Experimental results show that non-geometric binary crossover improves their search ability. Then we examine its effect on EMO algorithms. Experimental results show that non-geometric binary crossover drastically increases the diversity of solutions while it slightly degrades their convergence to the Pareto front. As a result, some performance measures such as hypervolume are clearly improved.
    Genetic and Evolutionary Computation Conference, GECCO 2007, Proceedings, London, England, UK, July 7-11, 2007; 01/2007
  • Journal of Japan Society for Fuzzy Theory and Intelligent Informatics 01/2006; 18(6):867-873.
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    ABSTRACT: We examine the effect of spatial structures on the evolution of iterated prisoner's dilemma (IPD) game strategies through computational experiments in single-dimensional and two-dimensional grid-worlds. Our computational experiments have two characteristic features. One is the use of a random pairing scheme in the IPD game where each player plays against a different randomly chosen opponent at every round of the dilemma game. The random pairing scheme makes it very difficult for players to evolve cooperative behavior. The other characteristic feature is the use of two neighborhood structures, which follows the concept of structured demes. One is for the interaction among players through the IPD game. A player in each cell in a grid-world plays against its neighbors defined by this neighborhood structure. The other is for the mating of strategies by genetic operations. A new strategy for a player is generated by genetic operations from a pair of parent strings, which are selected from its neighbors defined by the second neighborhood structure. It is shown that cooperative behavior is evolved only when the interaction neighborhood is very small and the mating neighborhood is small.
    01/2006;
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    ABSTRACT: We consider a traffic flow model where the information about the actual travel time for each alternative route is not available when each driver performs route selection. For such a traffic flow model, we examine two routing methods to minimize the average travel time over all vehicles running in the model. One method tries to minimize the average travel time globally. It is assumed in this method that a central manager determines the routes of all vehicles. Since the number of combinations of vehicles' routes exponentially increases as the number of vehicles increases, we need an efficient combinatorial optimization technique. In this paper, we employ a genetic algorithm to search for a near-optimal route combination for all vehicles. In the other method, each driver tries to minimize his/her own travel time locally with no central manager. It is assumed in this method that each driver selects the route with the shortest estimated travel time among alternative routes. Each driver uses a neural network for the travel time estimation. Through computational experiments, we clearly demonstrate the characteristic features of each method.
    01/2005;
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    ABSTRACT: Inter-Vehicle Communication (IVC) is a promising technology for the next-generation of auto industry. In this paper, we examine the effectiveness of an IVC application for route selection. First we develop a traffic simulator based on a microscopic traffic flow model using cellular automata. Our simulation environment is a simple two-way road with traffic signals. We implement four route selection methods. They are an IVC-based route selection method, a Memory-based route selection method without traffic information, a centralized traffic information-based route selection method like Vehicle Information and Communication System (VICS), and a random route selection method. Next we examine the effectiveness of each method under various settings. Simulation results show that traffic information plays an important role in traffic congestion avoidance. The traffic, however, becomes heavier as the number of vehicles using the centralized traffic information increases. On the other hand, the traffic flow becomes smoother as the number of vehicles using IVC increases. Simulation results also show that the use of IVC does not cause the disturbance of the whole traffic flow. We show that IVC has a possibility of resolving the difficulties of VICS and social traffic problems.
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    ABSTRACT: Elitism often has a large effect on the search ability of evolutionary algorithms. Many studies, however, did not discuss its implementation in cellular algorithms where a population of individuals is spatially distributed over a two-dimensional grid-world. In this paper, we examine two implementation schemes of elitism in cellular algorithms. One is global elitism where a number of the best individuals in the entire population are viewed as elite. The other is local elitism where an individual is viewed as elite when it is the best individual among its neighbors. Effects of elitism on the behavior of cellular algorithms are examined through performance evaluation, takeover time analysis, and diversity analysis. We use a cellular genetic algorithm with two neighborhood structures. One is for local competition among neighbors (e.g., fight for water and sunlight in the case of biological evolution of plants). The definition of local elitism is based on this competition neighborhood. The other is for local selection of parents from neighboring individuals. Since we have these two different neighborhood structures, we can specify the size of local competition for elitism independently of the size of local selection. Experimental results show that the choice of an implementation scheme of elitism has a dominant effect on the performance of our cellular genetic algorithm while it has only a slight effect on the takeover time. Good results are obtained under local elitism when the selection neighborhood is larger than the competition neighborhood. This relation in the size of the neighborhood structures